# The Verification Crisis: Expert Perceptions of GenAI Disinformation and the Case for Reproducible Provenance

arXiv: https://arxiv.org/abs/2602.02100 · HTML: https://mlsanity.com/paper/2602.02100/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 14 references that are in the map.
- First posted on arXiv 2 Feb 2026.

## Builds on (most cited first, 4 of 4)

- [Defending Against Neural Fake News](https://mlsanity.com/paper/1905.12616/) (2019)
- [The science of fake news](https://mlsanity.com/paper/2307.07903/) (2023)
- [Industrialized Deception: The Collateral Effects of LLM-Generated Misinformation on Digital Ecosystems](https://mlsanity.com/paper/2601.21963/) (2026)
- [Computational Reproducibility in Computational Social Science](https://mlsanity.com/paper/2307.01918/) (2023)

## Built on by (most cited first, 1 of 1)

- [Interrupting the Chain: Human Perception of AI-Generated Disinformation Through a Kill Chain Lens](https://mlsanity.com/paper/2608.21389/) (2026)

## Abstract (arXiv)

The growth of Generative Artificial Intelligence (GenAI) has shifted disinformation production from manual fabrication to automated, large-scale manipulation. This article presents findings from the first wave of a longitudinal expert perception survey (N=21) involving AI researchers, policymakers, and disinformation specialists. It examines the perceived severity of multimodal threats -- text, image, audio, and video -- and evaluates current mitigation strategies. Results indicate that while deepfake video presents immediate "shock" value, large-scale text generation poses a systemic risk of "epistemic fragmentation" and "synthetic consensus," particularly in the political domain. The survey reveals skepticism about technical detection tools, with experts favoring provenance standards and regulatory frameworks despite implementation barriers. GenAI disinformation research requires reproducible methods. The current challenge is measurement: without standardized benchmarks and reproducibility checklists, tracking or countering synthetic media remains difficult. We propose treating information integrity as an infrastructure with rigor in data provenance and methodological reproducibility.